Hi Joseph,
I've just tried that out. MLLib indeed returns different models. I see
no problem here then. How can Filipp's issue be possible?
Best,
Valeriy.
On 04/27/2018 10:00 PM, Valeriy Avanesov wrote:
Hi all,
maybe I'm missing something, but from what was discussed here I've
gathered that the current mllib implementation returns exactly the
same model whether standardization is turned on or off.
I suggest to consider an R script (please, see below) which trains two
penalized logistic regression models (with glmnet) with and without
standardization. The models are clearly different.
Therefore, the current mllib implementation doesn't follow glmnet.
library(glmnet)
library(e1071)
set.seed(13)
# generate synthetic data
X = cbind(-500:500, (-500:500)*1000)/1000
y = sigmoid(X %*% c(1, 1))
y = rbinom(y, 1, y)
# define two testing points
xTest = rbind(c(-10, -10000)/1000, c(-20, -20000)/1000)
# train two models: with and without standartization
lambda = 0.01
model = glmnet(X, y, family="binomial", standardize=TRUE, lambda=lambda)
print(predict(model, xTest, type="link"))
model = glmnet(X, y, family="binomial", standardize=FALSE, lambda=lambda)
print(predict(model, xTest, type="link"))
Best,
Valeriy.
On 04/25/2018 12:32 AM, DB Tsai wrote:
As I’m one of the original authors, let me chime in for some comments.
Without the standardization, the LBFGS will be unstable. For example,
if a feature is being x 10, then the corresponding coefficient should
be / 10 to make the same prediction. But without standardization, the
LBFGS will converge to different solution due to numerical stability.
TLDR, this can be implemented in the optimizer or in the trainer. We
choose to implement in the trainer as LBFGS optimizer in breeze
suffers this issue. As an user, you don’t need to care much even you
have one-hot encoding features, and the result should match R.
DB Tsai | Siri Open Source Technologies [not a contribution] |
Apple, Inc
On Apr 20, 2018, at 5:56 PM, Weichen Xu <weichen...@databricks.com
<mailto:weichen...@databricks.com>> wrote:
Right. If regularization item isn't zero, then enable/disable
standardization will get different result.
But, if comparing results between R-glmnet and mllib, if we set the
same parameters for regularization/standardization/... , then we
should get the same result. If not, thenmaybe there's a bug. In this
case you can paste your testing code and I can help fix it.
On Sat, Apr 21, 2018 at 1:06 AM, Valeriy Avanesov <acop...@gmail.com
<mailto:acop...@gmail.com>> wrote:
Hi all.
Filipp, do you use l1/l2/elstic-net penalization? I believe in
this case standardization matters.
Best,
Valeriy.
On 04/17/2018 11:40 AM, Weichen Xu wrote:
Not a bug.
When disabling standadization, mllib LR will still do
standadization for features, but it will scale the coefficients
back at the end (after training finished). So it will get the
same result with no standadization training. The purpose of it
is to improve the rate of convergence. So the result should be
always exactly the same with R's glmnet, no matter enable or
disable standadization.
Thanks!
On Sat, Apr 14, 2018 at 2:21 AM, Yanbo Liang
<yblia...@gmail.com <mailto:yblia...@gmail.com>> wrote:
Hi Filipp,
MLlib’s LR implementation did the same way as R’s glmnet
for standardization.
Actually you don’t need to care about the implementation
detail, as the coefficients are always returned on the
original scale, so it should be return the same result as
other popular ML libraries.
Could you point me where glmnet doesn’t scale features?
I suspect other issues cause your prediction quality
dropped. If you can share the code and data, I can help to
check it.
Thanks
Yanbo
On Apr 8, 2018, at 1:09 PM, Filipp Zhinkin
<filipp.zhin...@gmail.com
<mailto:filipp.zhin...@gmail.com>> wrote:
Hi all,
While migrating from custom LR implementation to MLLib's
LR implementation my colleagues noticed that prediction
quality dropped (accoring to different business metrics).
It's turned out that this issue caused by features
standardization perfomed by MLLib's LR: disregard to
'standardization' option's value all features are scaled
during loss and gradient computation (as well as in few
other places):
https://github.com/apache/spark/blob/6cc7021a40b64c41a51f337ec4be9545a25e838c/mllib/src/main/scala/org/apache/spark/ml/optim/aggregator/LogisticAggregator.scala#L229
<https://github.com/apache/spark/blob/6cc7021a40b64c41a51f337ec4be9545a25e838c/mllib/src/main/scala/org/apache/spark/ml/optim/aggregator/LogisticAggregator.scala#L229>
According to comments in the code, standardization should
be implemented the same way it was implementes in R's
glmnet package. I've looked through corresponding Fortran
code, an it seems like glmnet don't scale features when
you're disabling standardisation (but MLLib still does).
Our models contains multiple one-hot encoded features and
scaling them is a pretty bad idea.
Why MLLib's LR always scale all features? From my POV it's
a bug.
Thanks in advance,
Filipp.
